This work seeks to study the potential effectiveness of the Blind Signal Extraction (BSE) as a pre-processing tool for the detection of distributed faults in rolling bearings. In the literature, most of the authors focus their attention on the detection of incipient localized defects. In that case, classical techniques (i.e. envelope analysis) are robust in recognizing the presence of the fault and its characteristic frequency. However, when the fault grows, the classical approach fails, due to the change of the fault signature. De facto, in this case the signal does not contain impulses at the fault characteristic frequency, but more complex components with strong non-stationary contents. Moreover, signals acquired from complex machines often contain contributions from several different components as well as noise; thus the fault signature can be hidden in the complex system vibration. Therefore, pre-processing tools are needed in order to extract the bearing signature, from the raw system vibration. In this paper the authors focalize their attention on the application of the BSE in order to extract the bearing signature from the raw vibration of mechanical systems. The effectiveness and sensitivity of BSE is here exploited on the basis of both simulated and real signals. Among different procedures for the BSE computation, the Reduced-Rank Cyclic Regression algorithm (RRCR) is used. Firstly a simulated signal including the effect of gear meshing as well as a localized fault in bearings is introduced in order to tune the parameters of the RRCR. Next, two different real cases are considered, a bearing test-rig as an example of simple machine and a gearbox test-rig as an example of complex machine. In both examples, the bearings were degreased in order to accelerate the wear process. The BSE is compared with the usual pre-processing technique for the analysis of cyclostationary signals, i.e. the extraction of the residual signal. The fault detection is carried out by the computation of the Integrated Cyclic Modulation Spectrum on the extracted signals. The results indicate that the extracted signals via BSE clearly highlight the distributed fault signature, in particular both the appearance of the faults as well as their development are detected, whilst noise still hides fault grow in the residual signals.
Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings / D'Elia, G.; Cocconcelli, Marco; Mucchi, E.; Dalpiaz, G.. - In: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART C, JOURNAL OF MECHANICAL ENGINEERING SCIENCE. - ISSN 0954-4062. - 231:6(2017), pp. 1113-1128.
|Data di pubblicazione:||2017|
|Data di prima pubblicazione:||10-mar-2016|
|Titolo:||Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings|
|Autore/i:||D'Elia, G.; Cocconcelli, Marco; Mucchi, E.; Dalpiaz, G.|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1177/0954406216636165|
|Codice identificativo ISI:||WOS:000394785400008|
|Codice identificativo Scopus:||2-s2.0-85013018752|
|Citazione:||Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings / D'Elia, G.; Cocconcelli, Marco; Mucchi, E.; Dalpiaz, G.. - In: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART C, JOURNAL OF MECHANICAL ENGINEERING SCIENCE. - ISSN 0954-4062. - 231:6(2017), pp. 1113-1128.|
|Tipologia||Articolo su rivista|
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